Sensor Intelligence Engineer II (Embedded)

Whoop•Boston, MA

5d•Onsite

About The Position

At WHOOP, we're on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. As a Sensor Intelligence Engineer at WHOOP, youâll be part of a core team developing algorithms, hardware, and firmware that connect sensor data to health metrics which are foundational to WHOOP. This role will focus on optimizing low-level sensor interfaces to maximize battery life while balancing performance. This will involve developing low-level hardware-control algorithms in C and Python, building tools and pipelines to analyze data, optimizing firmware, and working closely with the embedded and hardware teams at WHOOP.

Requirements

Bachelor's or Masterâs degree in an engineering-related field with at least 2-years of industry experience working with signal processing and/or embedded systems
Strong understanding of signal processing fundamentals and applications
Proficiency in C and Python
Experience with embedded systems and implementing signal processing or ML algorithms
Creative problem-solver with a passion for innovating, improving processes, and developing new tools from scratch

Nice To Haves

Knowledge of control theory as applied to sensor systems
Familiarity with bare-metal or RTOS-based development on ARM-M series
Strong understanding of how to map high-level algorithms to low-level implementation in C
Experience with biosensor systems or biomedical signal processing, in particular with optics systems such as photodiodes, LEDs, and analog components such as ADCs
Familiarity with Machine Learning algorithms and development
Experience with statistical analysis and hypothesis testing

Responsibilities

Optimize WHOOP wearables to maximize sensor performance vs power consumption. Develop and refine algorithms to control low-level hardware, interfacing with the embedded and hardware teams.
Design and implement cutting-edge signal processing and machine learning algorithms on low-power MCUs and/or DSP processors, with a specific focus on ARM Cortex-M processors.
Engage in firmware development involving developing new modules, re-architecting, and optimizing new and existing code for performance, taking advantage of hardware acceleration.
Develop new tools and frameworks to support and automate the analyses.
Conduct thorough analyses of sensor signal data, including SNR, noise characterization, and performance evaluation, to identify areas for improvement.
Efficiently convert algorithms from Python/MATLAB into C, ensuring seamless integration and enhanced efficiency.
Conduct performance testing and validation of signal processing algorithms in both simulation and real-world scenarios.
Document all designs, methodologies, and results thoroughly for knowledge sharing and future reference.